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Posted on Nov 1, 2023 • Updated on Nov 5, 2023

Data Engineering for Beginners: A Step-by-Step Guide.

INTRODUCTION.

With the influx of huge amounts of data from a multitude of sources, data engineering has become essential to the data ecosystem and organizations are looking to build and expand their team of data engineers. If you’re looking to pursue a career in data engineering, this guide is for you to learn more about data engineering and the role of a data engineer and gain familiarity with the essential data engineering concepts.

What Is Data Engineering?

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data.

What does a data engineer do?

Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

Roles of a data engineer;

Data engineers focus on collecting and preparing data for use by data scientists and analysts. They take on three main roles as follows:

1. Generalists.

Data engineers with a general focus typically work on small teams, doing end-to-end data collection, intake and processing. They may have more skill than most data engineers, but less knowledge of systems architecture. A data scientist looking to become a data engineer would fit well into the generalist role. A project a generalist data engineer might undertake for a small, metro-area food delivery service would be to create a dashboard that displays the number of deliveries made each day for the past month and forecasts the delivery volume for the following month.

2. Pipeline-centric engineers.

These data engineers typically work on a midsize data analytics team and more complicated data science projects across distributed systems. Midsize and large companies are more likely to need this role. A regional food delivery company might undertake a pipeline-centric project to create a tool for data scientists and analysts to search metadata for information about deliveries. They might look at distance driven and drive time required for deliveries in the past month, then use that data in a predictive algorithm to see what it means for the company's future business.

3. Database-centric engineers.

These data engineers are tasked with implementing, maintaining and populating analytics databases. This role typically exists at larger companies where data is distributed across several databases. The engineers work with pipelines, tune databases for efficient analysis and create table schemas using extract, transform, load (ETL) methods. ETL is a process in which data is copied from several sources into a single destination system.

Data engineer responsibilities.

• Extracting and integrating data from a variety of sources—data collection.
• Preparing the data for analysis: processing the data by applying suitable transformations to prepare the data for analysis and other downstream tasks. Includes cleaning, validating, and transforming data.
• Designing, building, and maintaining data pipelines that encompass the flow of data from source to destination.
• Design and maintain infrastructure for data collection, processing, and storage—infrastructure management.

Data Engineering Concepts.

Data sources and types.

As mentioned, we have incoming data from all resources across the spectrum: from relational databases and web scraping to news feeds and user chats. The data coming from these sources can be classified into one of the three broad categories:

• Structured data
• Semi-structured data
• Unstructured data

1. Structured data.

It has a well-defined schema. Examples include data in relational databases, spreadsheets etc.

2. Semi-structured data.

It has some structure but no rigid schema and typically has metadata tags that provide additional information. Examples include JSON and XML data, emails, zip files, and more

3. Unstructured data.

It lacks a well-defined schema. Examples include images, videos and other multimedia files, website data.

HOW TO BECOME A DATA ENGINEER.

Step 1: consider data engineer education and qualifications..

As the data engineer job has gained more traction, companies such as IBM and Hadoop vendor Cloudera Inc. have begun offering certifications for data engineering professionals. Some popular data engineer certifications include the following:

• Certified Data Professional is offered by the Institute for Certification of Computing Professionals, or ICCP, as part of its general database professional program. Several tracks are offered. Candidates must be members of the ICCP and pay an annual membership fee to take the exam.
• Cloudera Certified Professional Data Engineer verifies a candidate's ability to ingest, transform, store, and analyze data in Cloudera's data tool environment. Cloudera charges a fee for its four-hour test. It consists of five to 10 hands-on tasks, and candidates must get a minimum score of 70% to pass. There are no prerequisites, but candidates should have extensive experience.
• Google Cloud Professional Data Engineer tests an individual's ability to use machine learning models, ensure data quality, and build and design data processing systems. Google charges a fee for the two-hour, multiple choice exam. There are no prerequisites, but Google recommends having some experience with Google Cloud Platform.

As with many IT certifications, those in data engineering are often based on a specific vendor's product, and the trainings and exams focus on teaching people to use their software.

Certifications alone aren't enough to land a data engineering job. Experience is also necessary to be considered for a position. Other ways to break into data engineering include the following:

• University degrees. Useful degrees for aspiring data engineers include bachelor's degrees in applied mathematics, computer science, physics or engineering. Also, master's degrees in computer science or computer engineering can help candidates set themselves apart.
• Online courses. Inexpensive and free online courses are a good way to learn data engineering skills. There are many useful videos on YouTube, as well as free online courses and resources, such as the following six options:

a. Codecademy's Learn Python. Knowledge of Python is essential for data engineers. This course requires no prior knowledge.

b. Coursera's guide to Linux server management and security. This four-week course covers the Linux basics.

c. GitHub SQL Cheatsheet. This GitHub repository is consistently updated with SQL query examples.

d. O'Reilly data engineering e-books. Titles in the big data architecture section cover data engineering topics.

e. Udacity Data Engineering Nanodegree. Udacity's online learning offerings include a data engineering track.

3.Project-based learning. With this more practical approach to learning data engineering skills, the first step is to set a project goal and then determine which skills are necessary to reach it. The project-based approach is a good way to maintain motivation and structure learning.

4.Develop your communication skills. Last but not least, data engineers also need communication skills to work across departments and understand the needs of data analysts and data scientists as well as business leaders. Depending on the organization, data engineers may also need to know how to develop dashboards, reports, and other visualizations to communicate with stakeholders.

Step 2: BUILD YOUR DATA ENGINEER SKILLS.

Data engineers require a significant set of technical skills to address their highly complex tasks. However, it’s very difficult to make a detailed and comprehensive list of skills and knowledge to succeed in any data engineering role; in the end, the data science ecosystem is rapidly evolving, and new technologies and systems are constantly appearing. This means that data engineers must be constantly learning to keep pace with technological breakthroughs. Notwithstanding this, here is a non-exhaustive list of skills you’ll need to develop to become a data engineer:

Data Repositories: Data Warehouses, Data Lakes, and Data Marts.

The raw data collected from various sources should be staged in a suitable repository. You should already be familiar with databases—both relational and non-relational. But there are other data repositories, too.

Before we go over them, it'll help to learn about two data processing systems, namely, OLTP and OLAP systems:

OLTP or Online Transactional Processing systems:

Are used to store day-to-day operational data for applications such as inventory management. OLTP systems include relational databases that store data that can be used for analysis and deriving business insights.

OLAP or Online Analytical Processing systems

Are used to store large volumes of historical data for carrying out complex analytics. In addition to databases, OLAP systems also include data warehouses and data lakes (more on this shortly). The source and type of data often determine the choice of data repository.

Common data repositories:

Data warehouses:.

A data warehouse refers to a single comprehensive store house of incoming data.

Data lakes:

Data lakes allow to store all data types—including semi-structured and unstructured data—in their raw format without processing them. Data lakes are often the destination for ELT processes (which we’ll discuss shortly).

You can think of data mart as a smaller subsection of a data warehouse—tailored for a specific business use case common

Data lake houses:

Recently, data lake houses are also becoming popular as they allow the flexibility of data lakes while offering the structure and organization of data warehouses.

Data Pipelines:

Etl and elt processes.

Data pipelines encompass the journey of data—from source to the destination systems—through ETL and ELT processes.

ETL—Extract, Transform, and Load—process .

They includes the following steps:

• Extract data from various sources
• Transform the data—clean, validate, and standardize data
• Load the data into a data repository or a destination application
• ETL processes often have a data warehouse as the destination.

ELT—Extract, Load, and Transform

A variation of the ETL process where instead of extract, transform, and load, the steps are in the order: extract, load, and transform meaning the raw data collected from the source is loaded to the data repository—before any transformation is applied. This allows us to apply transformations specific to a particular application. ELT processes have data lakes as their destination.

Data engineers must also understand NoSQL databases and Apache Spark systems, which are becoming common components of data workflows. Data engineers should have a knowledge of relational database systems as well, such as MySQL and PostgreSQL. Another focus is Lambda architecture, which supports unified data pipelines for batch and real-time processing.

Business intelligence (BI) platforms and the ability to configure them are another important focus for data engineers. With BI platforms, they can establish connections among data warehouses, data lakes and other data sources. Engineers must know how to work with the interactive dashboards BI platforms use.

Although machine learning is more in the data scientist's or the machine learning engineer's skill set, data engineers must understand it, as well, to be able to prepare data for machine learning platforms. They should know how to deploy machine learning algorithms and gain insights from them.

knowledge of Unix-based operating systems (OS) is important. Unix, Solaris and Linux provide functionality and root access that other OSes -- such as Mac OS and Windows -- don't. They give the user more control over the OS, which is useful for data engineers.

Tools Data Engineers Should Know:

The list of tools data engineers should know can be overwhelming. But don’t worry, you do not need to be an expert at all of them to land a job as a data engineer. Before we go ahead with listing the various tools data engineers should know, it’s important to note that data engineering requires a broad set of foundational skills including the following:

Programming language: Intermediate to advanced proficiency in a programming language preferably one of Python, Scalar, and Java

Databases and SQL: Good understanding of database design and ability to work with databases both relational databases such as MySQL and PostgreSQL and non-relational databases such as MongoDB.

Command-line fundamentals: Familiarity with Shell scripting and data processing and the command line.

Knowledge of operating systems and networking.

Data warehousing fundamentals

Fundamentals of distributed systems

Even as you are learning the fundamental skills, be sure to build projects that demonstrate your proficiency. There’s nothing as effective as learning, applying what you’ve learned in a project, and learning more as you work on it!

In addition, data engineering also requires strong software engineering skills including version control, logging, and application monitoring. You should also know how you use containerization tools like Docker and container orchestration tools like Kubernetes.

Though the actual tools you use may vary depending on your organization, it's helpful to learn:

• dbt (data build tool) for analytics engineering
• Apache Spark for big data analysis and distributed data processing
• Airflow for data pipeline orchestration
• Fundamentals of cloud computing and working with at least one cloud provider such as AWS or Microsoft Azure.

Step 3: WORK ON YOUR DATA ENGINEER PORTFOLIO.

The next step to becoming a data engineer is to work on some projects that will demonstrate your skills and understanding of core subjects. You can check out our full guide on building a data science portfolio for some inspiration.

You’ll want to demonstrate the skills we’ve already outlined in order to impress potential employers, which means working on a variety of different projects. DataCamp Workspace provides a collaborative cloud-based notebook that allows you to work on your own projects, meaning you can analyze data, collaborate with others, and share insights.

You can also apply your knowledge to various data science projects, allowing you to solve real-world problems from your browser, while also contributing to your date engineering portfolio.

When you feel that you are ready to explore a specific business area of your choice, you may start focusing on gaining domain knowledge and making individual projects related to that particular sphere.

STEP 4: APPLY FOR YOUR FIRST JOB AS A DATA ENGINEER.

Data engineering is one of the most in-demand positions in the data science industry. From Silicon Valley big tech to small data-drive startups across sectors, businesses are looking to hire data engineers to help them scale and make the most of their data resources. At the same time, companies are having trouble finding the right candidates, given the broad and highly specialized skill set required to meet an organization's needs.

Given this particular context, there is no perfect formula to land your first data engineering job. In many cases, data engineers arrive in their position following a transition from other data science roles within the same company, such as data scientist or database administrator.

Instead, if you are looking for data engineering opportunities in job portals, an important thing to keep in mind is that there are many job openings that include to the title “data engineer”, including cloud data engineer, big data engineer, and data architect. The specific skills and requirements will vary from position to position, so the key is to find a closer match between what you know and what the company needs.

How can you increase your chances to get the job?

The answer is simple: keep learning. There are many pathways to deepen your expertise and broaden your data engineering toolkit. You may want to consider a specialized and flexible program for data science, such as our Data Engineer with Python track.

You could also opt for further formal education, whether it’s a bachelor’s degree in data science or computer science, a closely related field, or a master’s degree in data engineering.

In addition to education, practice is the key to success. Employers in the field are looking for candidates with unique skills and a strong command of software and programming languages. The more you train your coding skills in personal projects and try big data tools and frameworks, the more chances you will have to stand out in the application process. To prove your expertise, a good option is to get certified in data engineering.

Finally, if you are having difficulties finding your first job as a data engineer, consider applying for other entry-level data science positions. In the end, data science is a collaborative field with many topics and skills that are transversal across data roles. These positions will provide you with valuable insights and experience that will help you land your dream data engineering position.

STEP 5: PREPARE FOR THE DATA ENGINEERING INTERVIEW.

Data engineering interviews are normally broken down into technical and non-technical parts;

Your resume and experience

Recruiters will want to know your experiences that are related to the data engineering position. Make sure to highlight your previous work in data science positions and projects in your resume and prepare to provide full detail about them, as this information is critical for recruiters to assess your technical skills, as well as your problem-solving, communication, and project management.

Programming

This is probably the most stressful part of a data science interview. Generally, you will be asked to resolve a problem in a few lines of code within a short time, using Python or a data framework like Spark.

You will not go far in your data engineering career without solid expertise in SQL. That’s why, in addition to the programming test, you may be asked to solve a problem that involves using SQL. Typically, the exercise will consist of writing efficient queries to do some data processing in databases.

System design

This is the most conceptual part of the technical interview and probably the most difficult. Designing data architectures is one of the most impactful tasks of data engineers. In this part, you will be asked to design a data solution from end to end, which normally comprises three aspects: data storage, data processing, and data modeling.

Once you have completed the technical part, the last step of the data engineering interview will consist of a personal interview with one or more of your prospective team members. The goal? To discover who you are and how you would fit in the team.

But remember, the data engineer interview is a two-sided conversation, meaning that you should also pose questions to them to determine whether you could see yourself as a part of the team.

Data Engineer Salary Expectations

Data engineering is an emerging job, and it’s not always easy for recruiters to find the right candidates. Competition for this difficult-to-find talent is high among companies, and that translates into some of the highest salaries among data science roles.

Data engineering is one of the most in-demand jobs in the data science landscape and is certainly a great career choice for aspiring data professionals. If you are determined to become a data engineer but don’t know how to get started, we highly recommend you follow our career track Data Engineer with Python, which will give you the solid and practical knowledge you’ll need to become a data engineering expert.

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Data Engineering Concepts, Processes, and Tools

• 15 min read
• Data Science ,   Engineering
• Last updated: 13 Mar, 2023
• 1 Comment Share

Sharing top billing on the list of data science capabilities, machine learning , and artificial intelligence are not just buzzwords: Many organizations are eager to adopt them. But prior to building intelligent products, you need to gather and prepare data, that fuels AI. A separate discipline — data engineering, lays the necessary groundwork for analytics projects. Tasks related to it occupy the first three layers of the data science hierarchy of needs suggested by Monica Rogati .

Data science layers towards AI by Monica Rogati

In this article, we will look at the data engineering process, explain its core components and tools, and describe the role of a data engineer.

What is data engineering?

Data engineering is a set of operations to make data available and usable to data scientists, data analysts, business intelligence (BI) developers , and other specialists within an organization. It takes dedicated experts – data engineers – to design and build systems for gathering and storing data at scale as well as preparing it for further analysis.

Main concepts and tools of data engineering

Within a large organization, there are usually many different types of operations management software (e.g., ERP, CRM, production systems, etc.), all containing databases with varied information. Besides, data can be stored as separate files or pulled from external sources — such as IoT devices — in real time. Having data scattered in different formats prevents the organization from seeing a clear picture of its business state and running analytics.

Data engineering addresses this problem step by step.

Data engineering process

The data engineering process covers a sequence of tasks that turn a large amount of raw data into a practical product meeting the needs of analysts, data scientists, machine learning engineers , and others. Typically, the end-to-end workflow consists of the following stages.

A data engineering process in brief

Data ingestion (acquisition)  moves data from multiple sources — SQL and NoSQL databases, IoT devices , websites, streaming services, etc. — to a target system to be transformed for further analysis. Data comes in various forms and can be both structured and unstructured .

Data transformation adjusts disparate data to the needs of end users. It involves removing errors and duplicates from data, normalizing it, and converting it into the needed format.

Data serving delivers transformed data to end users — a BI platform, dashboard, or data science team . 

How data science teams work

Data flow orchestration provides visibility into the data engineering process, ensuring that all tasks are successfully completed. It coordinates and continuously tracks data workflows to detect and fix data quality and performance issues.

The mechanism that automates ingestion, transformation, and serving steps of the data engineering process is known as a data pipeline.

Data engineering pipeline

A data pipeline combines tools and operations that move data from one system to another for storage and further handling. Constructing and maintaining data pipelines is the core responsibility of data engineers. Among other things, they write scripts to automate repetitive tasks – jobs .

Commonly, pipelines are used for

• data migration between systems or environments (from on-premises to cloud databases);
• data wrangling or converting raw data into a usable format for analytics, BI, and machine learning projects ;
• data integration from various systems and IoT devices; and
• copying tables from one database to another.

To learn more, read our detailed explanatory post — Data Pipeline: Components, Types, and Use Cases . Or stay here to briefly explore common types of data pipelines.

ETL pipeline

ETL (Extract, Transform, Load) pipeline is the most common architecture that has been here for decades. It’s often implemented by a dedicated specialist — an ETL developer .

As the name suggests, an ETL pipeline automates the following processes.

• Extract — retrieving data. At the start of the pipeline, we’re dealing with raw data from numerous sources — databases, APIs , files, etc.
• Transform — standardizing data. Having data extracted, scripts transform it to meet the format requirements. Data transformation significantly improves data discoverability and usability.
• Load — saving data to a new destination. After bringing data into a usable state, engineers can load it to the destination, typically a database management system (DBMS) or data warehouse.

ETL operations

Once the data is transformed and loaded into a centralized repository, it can be used for further analysis and business intelligence operations, i.e., generating reports, creating visualizations , etc. The specialist implementing ETL pipelines

ELT pipeline

An ELT pipeline performs the same steps but in a different order — Extract, Load, Transform. Instead of transforming all the collected data, you place it into a data warehouse, data lake, or data lakehouse . Later, you can process and format it fully or partially, once or numerous times.

ELT operations

ELT pipelines are preferable when you want to ingest as much data as possible and transform it later, depending on the needs arising. Unlike ETL, the ELT architecture doesn’t require you to decide on data types and formats in advance. In large-scale projects, two types of data pipelines are often combined to enable both traditional and real-time analytics . Also, two architectures can be involved to support Big Data analytics .

Read our article  ETL vs ELT: Key differences to dive deeper into the subject.

Data pipeline challenges

Setting up secure and reliable data flow is challenging. Many things can go wrong during data transportation: Data can be corrupted, hit bottlenecks causing latency, or data sources may conflict, generating duplicate or incorrect data. Getting data into one place requires careful planning and testing to filter out junk data, eliminating duplicates and incompatible data types to obfuscate sensitive information while not missing critical data.

Juan De Dios Santos , an experienced practitioner in the data industry, outlines two major pitfalls in building data pipelines:

• lacking relevant metrics and
• underestimating data load.

“The importance of a healthy and relevant metrics system is that it can inform us of the status and performance of each pipeline stage while underestimating the data load, I am referring to building the system in such a way that it won’t face any overload if the product experiences an unexpected surge of users,” elaborates Juan.

Besides a pipeline, a data warehouse must be built to support and facilitate data science activities. Let’s see how it works.

Data warehouse

A data warehouse (DW) is a central repository storing data in queryable forms. From a technical standpoint, a data warehouse is a relational database optimized for reading, aggregating, and querying large volumes of data. Traditionally, DWs only contained structured data or data that can be arranged in tables. However, modern DWs can also support unstructured data (such as images, pdf files, and audio formats).

Without DWs, data scientists would have to pull data straight from the production database and may report different results to the same question or cause delays and even outages. Serving as an enterprise’s single source of truth, the data warehouse simplifies the organization’s reporting and analysis, decision-making, and metrics forecasting.

Surprisingly, DW isn’t a regular database. How so?

First of all, they differ in terms of data structure. A typical database normalizes data excluding any data redundancies and separating related data into tables. This takes up a lot of computing resources, as a single query combines data from many tables. Contrarily, a DW uses simple queries with few tables to improve performance and analytics.

Second, aimed at day-to-day transactions, standard transactional databases don’t usually store historical data, while for warehouses, it’s their primary purpose, as they collect data from multiple periods. DW simplifies a data analyst’s job, allowing for manipulating all data from a single interface and deriving analytics, visualizations, and statistics.

Data vault architecture is a method of building a data warehouse. Our dedicated article covers this approach in detail; read it to learn more.

Data architecture with a data warehouse

To construct a data warehouse, four essential components are combined.

Data warehouse storage. The foundation of data warehouse architecture is a database that stores all enterprise data allowing business users to access it to draw valuable insights.

Data architects usually decide between on-premises and cloud-hosted DWs noting how the business can benefit from this or that solution. Although the cloud environment is more cost-efficient, easier to scale up or down, and isn’t limited to a prescribed structure, it may lose to on-prem solutions regarding querying speed and security. We’re going to list the most popular tools further on.

A data architect can also design collective storage for your data warehouse – multiple databases running in parallel. This will improve the warehouse’s scalability.

Metadata. Adding business context to data, metadata helps transform it into comprehensible knowledge. Metadata defines how data can be changed and processed. It contains information about any transformations or operations applied to source data while loading it into the data warehouse.

Data warehouse access tools. These instruments vary in functionality. For example, query and reporting tools are used for generating business analysis reports. And data mining tools automate finding patterns and correlations in large amounts of data based on advanced statistical modeling techniques.

Data warehouse management tools. Spanning the enterprise, the data warehouse deals with a number of management and administrative operations. Dedicated data warehouse management tools exist to accomplish this.

For more detailed information, visit our dedicated post — Enterprise Data Warehouse: EDW Components, Key Concepts, and Architecture Types .

Data warehouses are a significant step forward in enhancing your data architecture. However, DWs can be too bulky and slow to operate if you have hundreds of users from different departments. In this case, data marts can be built and implemented to increase speed and efficiency.

Simply speaking, a data mart is a smaller data warehouse (their size is usually less than 100Gb.). They become necessary when the company and the amount of its data grow and it becomes too long and ineffective to search for information in an enterprise DW. Instead, data marts are built to allow different departments (e.g., sales, marketing, C-suite) to access relevant information quickly and easily.

The place of data marts in the data infrastructure

There are three main types of data marts.

Dependent data marts are created from an enterprise DW and used as a primary source of information (also known as a top-down approach).

Independent data marts are standalone systems that function without DWs extracting information from various external and internal sources (it’s also known as a top-down approach).

Hybrid data marts combine information from both DW and other operational systems.

So, the main difference between data warehouses and data marts is that a DW is a large repository that holds all company data extracted from multiple sources, making it difficult to process and manage queries. Meanwhile, a data mart is a smaller repository containing a limited amount of data for a particular business group or department.

If you want to learn more, read our comprehensive overview — Data Marts: What They Are and Why Businesses Need Them .

While data marts allow business users to quickly access the queried data, often just getting the information is not enough. It has to be efficiently processed and analyzed to get actionable insights that support decision-making. Looking at your data from different perspectives is possible thanks to OLAP cubes. Let’s see what they are.

OLAP and OLAP cubes

OLAP or Online Analytical Processing refers to the computing approach allowing users to analyze multidimensional data. It’s contrasted with OLTP or Online Transactional Processing, a simpler method of interacting with databases, not designed for analyzing massive amounts of data from different perspectives.

Traditional databases resemble spreadsheets, using the two-dimensional structure of rows and columns. However, in OLAP, datasets are presented in multidimensional structures -- OLAP cubes . Such structures enable efficient processing and advanced analysis of vast amounts of varied data. For example, a sales department report would include such dimensions as product, region, sales representative, sales amount, month, and so on.

Information from DWs is aggregated and loaded into the OLAP cube, where it gets precalculated and is readily available for user requests.

Within OLAP, data can be analyzed from multiple perspectives. For example, it can be drilled down/rolled up if you need to change the hierarchy level of data representation and get a more or less detailed picture. You can also slice information to segment a particular dataset as a separate spreadsheet or dice it to create a different cube. These and other techniques enable finding patterns in varied data and creating a wide range of reports.

It’s important to note that OLAP cubes must be custom-built for every report or analytical query. However, their usage is justified since, as we said, they facilitate advanced, multidimensional analysis that was previously too complicated to perform.

Read our article What is OLAP: A Complete Guide to Online Analytical Processing for a more detailed explanation.

Big data engineering

Speaking about data engineering, we can’t ignore Big Data. Grounded in the four Vs – volume, velocity, variety, and veracity – it usually floods large technology companies like YouTube, Amazon, or Instagram. Big Data engineering is about building massive reservoirs and highly scalable and fault-tolerant distributed systems.

Big data architecture differs from conventional data handling, as here we’re talking about such massive volumes of rapidly changing information streams that a data warehouse can’t accommodate. That’s where a data lake comes in handy.

A data lake is a vast pool for saving data in its native, unprocessed form. It stands out for its high agility as it isn’t limited to a warehouse’s fixed configuration.

Big data architecture with a data lake

A data lake uses the ELT approach and starts data loading immediately after extracting it, handling raw — often unstructured — data.

A data lake is worth building in those projects that will scale and need a more advanced architecture. Besides, it’s very convenient when the purpose of the data hasn’t been determined yet. In this case, you can load data quickly, store it, and modify it as necessary.

Data lakes are also a powerful tool for data scientists and ML engineers, who would use raw data to prepare it for predictive analytics and machine learning. Read more about data preparation in our separate article or watch this 14-minute explainer.

How to prepare datasets for machine learning projects

Lakes are built on large, distributed clusters that would be able to store and process masses of data. A famous example of such a data lake platform is Hadoop.

Hadoop and its ecosystem

Hadoop is a large-scale, Java-based data processing framework capable of analyzing massive datasets. The platform facilitates splitting data analysis jobs across various servers and running them in parallel. It consists of three components:

• Hadoop Distributed File System (HDFS) capable of storing Big Data,
• a processing engine MapReduce, and
• a resource manager YARN to control and monitor workloads.

Also, Hadoop benefits from a vast ecosystem of open-source tools that enhance its capabilities and address various challenges of Big Data.

Hadoop ecosystem evolvement

Some popular instruments within the Hadoop ecosystem are

• HBase, a NoSQL database built on top of HDFS that provides real-time access to read or write data;
• Apache Pig, Apache Hive, Apache Drill, and Apache Phoenix to simplify Big Data exploration and analysis when working with HBase, HDFS, and MapReduce; and
• Apache Zookeeper and Apache Oozie to coordinate operations and schedule jobs across a Hadoop cluster.

Read about the advantages and pitfalls of Hadoop in our dedicated article The Good and the Bad of Hadoop Big Data Framework .

Streaming analytics instruments

Tools enabling streaming analytics form a vital group within the Hadoop ecosystem. These include

• Apache Spark , a computing engine for large datasets with near-real-time processing capabilities;
• Apache Storm, a real-time computing system for unbounded streams of data (those that have a start but no defined end and must be continuously processed);
• Apache Flink processing both unbounded and bounded data streams (those with a defined start and end); and
• Apache Kafka, a streaming platform for messaging, storing, processing, and integrating large volumes of data.

Kafka and data streaming, explained

All these technologies are used to build real-time Big Data pipelines. You can get more information from our articles Hadoop vs Spark: Main Big Data Tools Explained and The Good and the Bad of Apache Kafka Streaming Platform .

Enterprise data hub

When a big data pipeline is not managed correctly, data lakes quickly become data swamps – a collection of miscellaneous data that is neither governable nor usable. A new data integration approach called a data hub emerged to tackle this problem.

Enterprise data hubs (EDHs) are the next generation of data architecture aiming at sharing managed data between systems. They connect multiple sources of information, including DWs and data lakes. Unlike DWs, the data hub supports all types of data and easily integrates systems . Besides that, it can be deployed within weeks or even days while DW deployment can last months and even years.

At the same time, data hubs come with additional capabilities for data management , harmonizing, exploration, and analysis — something data lakes lack. They are business-oriented and tailored for the most urgent organization’s needs.

To sum it all up,

• a data warehouse is constructed to deal mainly with structured data for the purpose of self-service analytics and BI;
• a data lake is built to deal with sizable aggregates of both structured and unstructured data to support deep learning, machine learning, and AI in general; and
• a data hub is created for multi-structured data portability, easier exchange, and efficient processing.

An EDH can be integrated with a DW and/or a data lake to streamline data processing and deal with these architectures' everyday challenges.

Read our article  What is Data Hub: Purpose, Architecture Patterns, and Existing Solutions Overview to learn more.

Role of data engineer

Now that we know what data engineering is concerned with, let’s delve into the role that specializes in creating software solutions around big data –a data engineer.

Juan De Dios Santos, a data engineer himself, defines this role in the following way: “ In a multidisciplinary team that includes data scientists, BI engineers, and data engineers, the role of the data engineer is mostly to ensure the quality and availability of the data.” He also adds that a data engineer might collaborate with others when implementing or designing a data-related feature (or product) such as an A/B test, deploying a machine learning model, and refining an existing data source.

We have a separate article explaining what a data engineer is , so here we’ll only briefly recap.

Skills and qualifications

Data engineering lies at the intersection of software engineering and data science, which leads to skill overlapping.

Overlapping skills of the software engineer, data engineer, and data scientist. Source: Ryan Swanstrom

Software engineering background. Data engineers use programming languages to enable reliable and convenient access to data and databases. Juan points out their ability to work with the complete software development cycle, including ideation, architecture design, prototyping , testing, deployment, DevOps , defining metrics, and monitoring systems. Data engineers are experienced programmers in at least Python or Scala/Java.

Data-related skills. “ A data engineer should have knowledge of multiple kinds of databases (SQL and NoSQL), data platforms, concepts such as MapReduce, batch and stream processing, and even some basic theory of data itself, e.g., data types, and descriptive statistics, ” underlines Juan.

Systems creation skills. Data engineers need experience with various data storage technologies and frameworks they can combine to build data pipelines.

A data engineer should have a deep understanding of many data technologies to be able to choose the right ones for a specific job.

Airflow . This Python-based workflow management system was developed by Airbnb to rearchitect its data pipelines. Migrating to Airflow, the company reduced their experimentation reporting framework (ERF) run-time from 24+ hours to about 45 minutes. Among the Airflow’s pros, Juan highlights its operators : “They allow us to execute bash commands, run a SQL query or even send an email.” Juan also stresses Airflow’s ability to send Slack notifications, complete and rich UI, and the overall maturity of the project . On the contrary, Juan dislikes that Airflow only allows for writing jobs in Python .

Read our article The Good and the Bad of Apache Airflow Pipeline Orchestration to learn more.

Cloud Dataflow . A cloud-based data processing service, Dataflow is aimed at large-scale data ingestion and low-latency processing through fast parallel execution of the analytics pipelines. Dataflow is beneficial over Airflow as it supports multiple languages like Java , Python, SQL, and engines like Flink and Spark. It is also well maintained by Google Cloud. However, Juan warns that Dataflow’s high cost might be a disadvantage.

Other popular instruments are the Stitch platform for rapidly moving data and Blendo , a tool for syncing various data sources with a data warehouse.

Warehouse solutions. Widely used on-premise data warehouse tools include Teradata Data Warehouse , SAP Data Warehouse , IBM db2 , and Oracle Exadata . The most popular cloud-based data warehouse solutions are Amazon Redshift and Google BigQuery . Be sure to check our detailed comparison of the top cloud warehouse software .

Big data tools. Technologies that a data engineer should master (or at least know of) are Hadoop and its ecosystem, Elastic Stack for end-to-end big data analytics, data lakes, and more.

Data engineer vs data scientist

It’s not rare that a data engineer is confused with a data scientist . We asked Alexander Konduforov , a data scientist with over ten years of experience, to comment on the difference between these roles.

“ Both data scientists and data engineers work with data but solve quite different tasks, have different skills, and use different tools ,” Alexander explained, “ Data engineers build and maintain massive data storage and apply engineering skills: programming languages, ETL techniques, knowledge of different data warehouses and database languages. Whereas data scientists clean and analyze this data, get valuable insights from it, implement models for forecasting and predictive analytics, and mostly apply their math and algorithmic skills, machine learning algorithms and tools. ”

Alexander stresses that accessing data can be a difficult task for data scientists for several reasons.

• Vast data volumes require additional effort and specific engineering solutions to access and process them in a reasonable amount of time.
• Data is usually stored in lots of different systems and formats. It makes sense first to take data preparation steps and move information to a central repository like a data warehouse makes sense. This is typically a task for data architects and engineers.
• Data repositories have different APIs for accessing them. Data scientists need data engineers to implement the most efficient and reliable pipeline for getting data.

As we can see, working with storage systems built and served by data engineers, data scientists become their “ internal clients. ” That’s where their collaboration takes place.

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Introduction to data engineering on Azure

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Slides: Data Quality, Data Engineering, and Data Science

Webinar: Data Quality, Data Engineering, and Data Science from DATAVERSITY To view the On Demand recording of this presentation, click HERE>> About the Webinar This webinar explores the organizational constructs and processes for enabling business to build better insights through Data Quality, Data Engineering, and Data Science.  In particular, it examines the needs for: A […]

To view the On Demand recording of this presentation, click HERE>>

About the webinar.

This webinar explores the organizational constructs and processes for enabling business to build better insights through Data Quality, Data Engineering, and Data Science.  In particular, it examines the needs for:

• A Data Lab to foster an open, questioning, and collaborative environment to develop the right data principles, patterns, and standards.
• A Data Factory to implement those standards developed in the Data Lab.
• Different Data Quality requirements in the Lab and Factory, how Data Engineering aims to meet both needs.
• Data Engineering, in advance of the sexier Data Science, to create the right environments in both the lab and the factory and to actually examine the data.
• All of the above to provide the data needed to create more efficient processes for the Data Scientists to be more effective in their roles.

Join this webinar to hear Tom “The Data Doc” Redman discuss with Dr. Prashanth Southekal, recent author of Data for Business Performance , the details of achieving better insights with examples of a case study from an Oil and Gas company.

About the Speakers

Thomas Redman

The Data Doc, Data Quality Solutions

Prashanth H Southekal, PhD.

Managing Principal, DBP-Institute

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Download our innovative Data Engineering presentation template for MS PowerPoint and Google Slides to describe the process of developing systems that allow users to collect, analyze and use raw data of various formats within organizations.

Data engineers can leverage these slides to demonstrate how data engineering simplifies data extraction from data sources and makes it available to end-users for deployment. You can harness the animate deck to depict the pipeline, primary operations, and systematic workflow of the data engineering process.

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What is data engineering? Key concepts, best practices and examples

Umama Rahman April 26th, 2022

What is data engineering?

• The three V-s simply define big data

Three main types of data

What are data pipelines.

• What does a data engineer do

Data engineering tools and technologies

Data engineering best practices, the future of data engineering.

Back in 2018, the International Data Corporation predicted that the total amount of data produced globally would exceed 175 zettabytes by 2025. Statista has made similar predictions. This massive volume of data must have some purpose to serve. Otherwise, nobody would be keeping track of these numbers.

The tons of data that we produce every minute of every day is of great importance to organizations of all types. Businesses gather valuable insights from these data and use them to streamline internal processes and provide improved services to consumers, among other things. However, randomly collected raw data itself might as well be gibberish.

The findings from a recent Research and Markets report indicate that businesses are expected to allocate a sizable portion of their resources and budgets in search of experts who can help them make sense of the different types of data that they collect from one or more sources. Organizations do not want their data projects to fail.

Before we talk about how and why data engineering services are becoming increasingly important for organizations of all sizes and industries, allow us to shed some light on what data engineering is. In this discussion, we will cover the following:

Data engineering refers to the process of data management, involving the setting up of certain mechanisms to aid data collection and storage. It provides a sense of structure to all the data that a business intends to collect and utilize to its benefit. The process of data engineering helps to convert haphazard, meaningless data into organized information.

Data engineering is the key to holistic business process management.

From finance and accounting to sales and customer support, a wide range of business functions relies heavily on data.

Data engineering makes it possible for businesses to extract important information from large datasets and make key decisions after careful data analysis.

The demand for data engineering services has skyrocketed over the past few years as the world has embraced big data.

The three V-s simply define big data:

These can help you identify whether the data you are dealing with can be classified as big data. If you collect data in large amounts that are produced over short periods and have a wide range of types, classes, labels, and/or formats, then you have a big data problem on your hands.

Structured data is strictly relational.

That means it can be conveniently stored in a relational database management system (RDBMS).

A traditional database stores data in row-column format with distinct identifiers, characteristics, and relationships established between one or more tables.

You might face significant trouble trying to store unstructured data – often in the form of text, audio, video, image, or other files – in an RDBMS.

For example, it can be logically difficult, inefficient, and/or expensive to store data from your Fitbit or live surveillance data in a table made of rows and columns.

This is where non-relational or “ NoSQL” databases come into play.

In the case of images, videos and similar, you rarely keep them in a database but store the metadata and file reference in the database, while the actual content is stored in a file system or object storage.

Semi-structured data falls somewhere between relational and non-relational data – it can be a combination of both.

Therefore, it needs to be stored in a non-relational database. Semi-structured data has some sort of structure but does not adhere to a fixed schema or data model.

It may contain metadata that helps organize it but it is not suitable for storage in a tabular format.

The people who are tasked with handling raw data and architecting its storage, infrastructure, and flow mechanisms are called data engineers. They are responsible for constructing and managing data pipelines, databases, data warehouses, and/or data lakes.

A data pipeline defines the mechanism that determines the flow of data from its origin to its destination, including the various processes or transformations that the data might undergo along the way. A standard pipeline is quite like one of the most basic computing processes of Input → Processing → Output. It comprises three basic parts that are:

The source or origin

Transference and/or processing mechanisms

The destination

• Data source

The source refers to the starting point of the data flow cycle, that is, the location where the data enters the pipeline. There are many types of data sources, such as relational and non-relational databases. They can collect many types of data, ranging from simple text-based answers obtained through a simple form on a website to sensor data collected from an IoT device e.g., smart home devices such as lightbulbs, air quality sensors, and thermostats.

When dealing with big data, traditional databases such as MS Access, MySQL, or other relational database management systems (DBMS) might not be capable of handling such large volumes, velocities, and varieties of data. Thus, organizations turn to larger, more flexible data stores that are specially built to handle big data. Some examples of this type of data store are d distributed file systems (e.g. HDFS), object storage, or databases that are specifically designed to handle big data (e.g. MongoDB, Kafka, Druid).

• Data transfer and/or processing

In a typical data pipeline, the second part of the pipeline is where the collected data transforms. Often, this type of pipeline is called an ETL data pipeline (Extract – Transform – Load). In this phase, the data collected from one or more sources undergoes some sort of processing to make it more useful and digestible.

There are two common methods to process data in big data engineering:

• Batch processing
• Stream processing

In batch p rocessing, data is pro cessed in batches of varying sizes. If the batches are very small – for example, only a couple of samples – it is called mini batching, but the batches can also be days’ worth of data.

I n contrast, stream processing revolves around processing data as it comes in, on the level of single samples. The system does not wait for the periodic build-up of a backlog. Instead, the processing happens in real-time. Big data engineering often incorporates a mix of batch and stream processing – Lambda architecture – as there can be multiple use cases to cater to.

• Destination

Where the data ends up after being processed is referred to as its destination. Just like the data source, the destination in a particular pipeline can also be of many types. Some pipelines are built to fetch data from various sources and process it to remove inconsistencies and organize it. This pipeline in particular might end up storing the refined data in another data store, such as a relational database. Another pipeline might be tasked with supplying processed data to a database that supplies data to a dashboard. Thus, the destination depends on the purpose for which the relayed or transformed data is to be used.

What does a data engineer do: The difference between data engineers and data scientists

Many people often confuse data engineers and data scientists . In recent times, we have even seen a bit of overlap between the two roles. However, there are some basic differences.

Data engineering, as we discussed before, pertains to handling raw data and setting up a suitable architecture to aid with refining or processing that data. Data engineers may have to think about things like how long the data needs to be stored, or which platform or type of data store would be best for the job. Their responsibilities also include the first round of data cleaning, checking for and fixing errors, and organizing the data to make it more presentable and easier to understand.

Data science, on the other hand, has more to do with the analytical part of data processing. Data scientists are one of the many end-users that ultimately utilize the data that is cleaned, processed, and supplied by the data engineers. They may run machine learning algorithms, complex statistical processes, or other analytical procedures to extract important insights from the data and use them for critical business decision-making.

Let us talk about the slightly more technical aspect of data engineering.

Some specific tools and technologies are used by many data engineers that empower them to work their magic. Below, we mention some of the most popular programming languages, databases, data warehouse solutions, and other big data technologies that are used for data engineering projects.

Programming languages

To enable communication between different systems (whether they are data storage or ETL processes), you need computer algorithms. Therefore, data engineers need to be skilled in certain programming languages.

The primary languages used by data engineers are SQL, Python, Java, and Scala. However, other languages (such as R, Julia, or Rust) are also used in some companies for performing certain tasks.

Relational and non-relational databases

These are the two broad categories of databases often used in data engineering projects. When the data to be stored is structured in nature, it can easily be stored in a relational database.

For semi- or unstructured data, businesses use non-relational or NoSQL databases.

There can be multiple databases of different types involved in a single project depending on the organization’s storage, processing, and/or data modeling needs.

Some popular SQL and NoSQL databases:

• Microsoft SQL Server
• Elasticsearch
• Apache Cassandra
• Apache HBase

In-memory databases

If you are dealing with a big data engineering problem or even just an extensive disk-based database that requires real-time operations, it is crucial to keep latency to a minimum.

For this purpose, certain caching systems need to be put in place. In-memory databases quicken the process to query data by eliminating disk I/O.

Some popular in-memory data stores:

• SingleStore
• Oracle TimesTen

Enterprise data warehouses

There are several pre-built data warehousing platforms available on the market today, ready for corporations to use.

Most of these data solutions are well-equipped to meet a business’s big data engineering needs.

Some popular data warehousing solution:

• Google BigQuery
• Amazon Redshift
• Azure Synapse Analytics

Workflow schedulers

To make data engineering processes faster and more efficient, data engineers often use workflow scheduling and automation tools for better data pipeline management.

With a scheduler, you can save time by automating repetitive tasks, save resources by eliminating redundant processes, easily modify the dataflow, and log each event as your data flows through the pipeline.

Some popular automation and scheduling tools:

• Apache Airflow
• Apache Oozie

Honorary mentions

Some other notable data engineering tools and technologies are listed below. These include file systems, object stores, pipeline construction platforms, querying services, big data analytics, and visualization platforms, business intelligence tools, and data integration tools: Apache Hadoop; Amazon S3 (Simple Storage Service); Apache Spark; Apache Kafka; Amazon Athena; dbt; Tableau; Power BI; Talend Open Studio; Informatica; Kubernetes; Kubeflow.

To make the best use of all available tools and technologies, it is vital to follow certain data engineering practices that will gain maximal returns for the business. Let’s talk about six of the top industry practices that set apart a good professional data engineer from an amazing one.

Tapping into existing skills

The data engineering field is still relatively new and undergoing many rapid developments every day. Thus, it can be hard to stay on top of all these advancements. The good news is that a lot of newer big data tools are built to work using methods already familiar to any data engineer.

Instead of investing in learning a brand-new technology or framework from scratch, you can go for tools that are based on traditional concepts. This will not only save precious resources that would otherwise be spent on extensive training but also prevent you from incurring losses in the form of project delays.

Here is an example from our project for Elisa , for which we conducted network log analysis using Google BigQuery. Under the hood, BigQuery works on a distributed file system with a query engine that manages the distribution of workloads on the nodes holding the data. For the end-user, though, it is just another dialect of SQL, enabling any engineer to work on this big data project.

Establishing high connectivity between systems

Due to the intricate and immensely interrelated nature of most data engineering systems, it is important to ensure that any dependencies are taken care of.

There are two solutions to this problem. The first one involves spending time and effort manually establishing connections between various systems. The easier, resource-efficient solution involves using tools that have built-in connectivity features.

This is a major reason why we extensively use Apache Airflow to manage data pipelines. Airflow features numerous operators and connectors that allow you to easily manage credentials for connections to various sources. Furthermore, it also covers a lot of the boilerplate code that big data engineers might otherwise need to write just to access data sources and sinks.

Enable extensibility within systems

Most transformations and procedures that must be conducted on data can be managed with most tools. It does happen, though, that some other tool or language is either needed or just more efficient or easy to implement to fit your need. To be ready for this highly likely scenario, you shouldn’t expect your chosen platform or tool set to handle every possible situation but keep some flexibility in the system by using easily extendable tools.

Almost all data pipelines written at MindTitan include a lot of machine learning functionality. As ML-driven algorithms can be quite varied in terms of the tools that they need, our pipeline orchestration tools need to be very nonrestrictive when it comes to what technologies can be used to execute the various steps in the pipeline. This is another reason why we extensively use Apache Airflow and, on occasion, Kubeflow.

Document everything

What is data engineering without proper documentation? Your big data project can quickly turn into a big mess.

When handling such large volumes of data, it can be very easy to lose track of what is going on. Proper documentation helps preserve critical information about what data is where, how it should be interpreted, and what fields should be used to join it with other data sources. Maintaining event logs ensures that all changes, no matter how minute or seemingly insignificant, are tracked. Documentation also helps with error detection, tracking, monitoring, and executing appropriate corrective measures.

Proper documentation helps not just the current data team(s) but also those that come after. Businesses might end up switching out platforms, tools, or entire engineering teams at any point in time. To introduce new technologies or teams, it is vital to understand the existing system, which cannot be done if there is no written record of all the happenings within the pipeline.

Prioritize streams over batches

Many businesses tend to rely heavily on batch processing as opposed to streaming. However, one needs to keep in mind the various drawbacks that batch processing can have. For use cases where it is required to process data in real-time, a longer than necessary delay can incur heavy losses or reduce profit margins significantly.

One option to tackle this problem is to switch your data models completely to streaming. However, if you cannot afford to make such a drastic change, you can go for a hybrid model. Another shortcut is to simulate streaming by reducing the intervals set for your batch processes to minimize latency.

For the fault monitoring system that we set up at Elisa, many processes occur at the same time. There is a constant flow of incoming calls on various topics, and it is vital to be notified of issues as early as possible. At Elisa, we use machine learning to classify calls into various categories, including issue reports on specific services. These data are fed to Kafka, which the anomaly detection system uses to detect potential issues and report them to the technical team in real-time.

Enable concurrency

As we discussed in the previous point, there is almost always a need for multiple processes to run within a single pipeline. Without proper control mechanisms, this can quickly lead to deadlocks or blocks. To avoid processes from hogging or competing for shared resources, it is important to establish concurrency within the data pipeline.

Consider our Elisa network log analysis module. The data is loaded into BigQuery, which is a distributed data processing tool that scales automatically to fit the load. The data engineers use it to run periodic machine learning workloads predicting customer experience index, and the data analysts run ad-hoc data analysis jobs as they come along.

For the near future, the data engineering team is most excited about the wider adoption of a low-maintenance and easy-to-use data stack.

A lot has been achieved, despite some tools being partially in development or lacking the polish that would make them “easy to use”.

But Markus believes that as we move forward and these technologies start to be widely adopted, it is going to enable data engineers to better deliver on data availability and processing performance, which influences everything from BI to AI solutions, making the data engineer’s job a lot more fun and providing significant value to the business.

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Today, all organizations are on an “information superhighway.” the sheer volumes of information exploited by technology have given rise to bundles of complexities. these increasing complexities have significant ramifications on how businesses manage and maintain data integrity as they become a data-driven enterprise. as more companies rely on data science to increase their business decisions’ velocity and veracity, clean, available, and reliable data becomes crucial. – powerpoint ppt presentation.

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DP-203 Data Engineering on Microsoft Azure

Jan 04, 2023

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Azure DP-203 Data Engineering on Microsoft Azure course by SkillUp Online is created to enable you to design, implement, operationalize, monitor and secure your data solutions on Microsoft Azure with hands-on labs. Learn more: https://skillup.online/courses/dp-203-data-engineering-on-microsoft-azure/<br>

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DP-203: Data Engineering on Microsoft Azure (Course) Course Overview

DP-203 Data Engineering on Microsoft Azure Course • Learn how Azure services work together to enable you to design, implement, operationalize, monitor, optimize, and secure data solutions on Microsoft Azure. • Benefit from instructor-led preparation for the Azure DP-203 certification exam with tips, tricks, guidance, and mentored support.

Course Overview For data professionals who are interested in learning about analytical solutions utilising Azure data platform technologies, there is a four-day course called AzureDP-203 called Data Engineering on Azure. You will study about data engineering in this course with regard to the fundamental computation and storage technologies that are employed while developing an analytical solution. You will learn how to use Azure Synapse Analytics' server less SQL pools to run interactive queries, as well as how to use Azure Data bricks and Apache Spark for data exploration and transformation. The best techniques for data loading in Azure Synapse Analytics will be explained to you. You will also learn how to use Azure Data Factory to integrate data and do petabyte-scale ingestion and code-free transformation.

How It Works Eleven specifically created modules make up this course, which guides you through a well-planned learning path.It is an instructor-led course with a predetermined start and end date and a set schedule. Your instructor is the one driving it ahead, and it includes live sessions that are broadcast at a specific time. However, you will have time outside of the live sessions to finish some tasks at your own leisure. From the beginning of the course and for the life of your enrolment, you can access the materials for each module. There will be reading material, practical laboratories, and online exam questions as learning and assessment tools.

Skills You Will Gain You will be able to: Design and implement data storage. Design and develop data processing. Design and implement data security. Monitor and optimize data storage and data processing.

Who Should Enroll On This Course Data engineers. Data architects. Business intelligence professionals. Data analysts and data scientists who work with analytical solutions built on Microsoft Azure will also find this useful. Professionals who are looking to clear their DP-203 certification exam.

How Will This Course Help Me To Prepare for the Microsoft Certification Exam? You will benefit from this course as you get ready to take the DP-203: Data Engineering on Microsoft Azure test. It is perfect for people who want to comprehend the fundamental computing and storage systems that go into creating an analytical solution. In order to construct analytics solutions, you will investigate integrating, converting, and consolidating data from diverse structured and unstructured data systems. Given a set of business needs and restrictions, you will discover how to make sure that data pipelines and data stores are highly effective, efficient, organised, and reliable. You will investigate Data Warehouse, Azure Synapse, Apache Spark, Azure Databricks, and Stream Analytics.

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InfoQ Homepage Presentations CI/CD beyond YAML: The Evolution Towards Pipelines-as-Code

CI/CD beyond YAML: The Evolution Towards Pipelines-as-Code

Conor Barber explores the evolution of infrastructure, focusing on the shift from YAML configurations to pipelines-as-code, covering modern CI/CD systems like GitHub Actions, GitLab, and CircleCI. This talk was recorded at QCon San Francisco 2023.

Conor Barber is a software engineer at Airbyte, bringing over a decade of experience in data and infrastructure engineering from leading tech companies. Previously at Apple, Conor developed scalable solutions for complex CI/CD processes involved in managing hundreds of connectors and the ELT platform that the connectors run on at Airbyte.

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Barber: We're going to talk about Bart and Lisa. Bart and Lisa, they can be anybody in the software world that's concerned with shipping software. Typically, this might be your engineer or platform engineer, or just like a startup founder. They've been hard at work making some awesome software, and they've got a prototype. They're ready, let's go, let's ship it. We want to build and test and deploy that software. We're going to talk about the stories of Bart and Lisa in that context. Let's start with Bart's story. On day 1, Bart has created an awesome service. We're going to talk a lot about GitHub Actions. We use GitHub Actions at Airbyte, but this talk is broadly applicable to many other CI/CD systems: GitLab, Jenkins. Bart's created a back office service, and he's got a CI for it. He's got a little bit of YAML he created, and he was able to get up and running really quickly. He's got this backup service: it's built, it's tested, it's deployed. Really quick, really awesome. That's day 1. Day 11, so Bart's just been cranking out code, he's doing great. He's got a back office service. He's got a cart service. He's got a config service. He's got an inventory service. He's got a lot of different things going on. He's got several different YAML's now. He's got one to build his back office. He needs to talk to the config service. It's starting to pile up, but it's still manageable. He can manage with these. This is day 101. Bart's got quite a few services now. He has too many things going on, he doesn't really know what to do. Every time that he makes a change, there's just all these different things going on, and he just doesn't feel like he can handle it anymore. What about Lisa? Lisa decided to do things a little bit differently. What Lisa has done is the subject of our talk. The title of our talk is CI/CD beyond YAML, or, how I learned to stop worrying about holding all these YAML's and scale.

I am a senior software engineer. I work on infrastructure at Airbyte. Airbyte is an ELT platform. It is concerned with helping people move data from different disparate sources such as Stripe, Facebook, Google Ads, Snap, into their data lake houses, data warehouses, whatnot. Why CI/CD is important for us at Airbyte is because part of Airbyte's draw is that there are a lot of different connectors, as we call them, a Stripe connector, a Google Ads connector. For each one of these, we build and test and deploy these as containers. The more that we have the heavier of a load on CI/CD that we have to contextualize. I became quite interested in this problem whenever I came to Airbyte, because at the beginning, it seemed almost intractable. There were a lot of different moving gears, and it seemed like there was just no way that we could get a handle on it. I want to tell you the story of how we managed to segment out this problem in such a way that we feel that we can scale our connector situation, and building our platform at Airbyte as well.

We'll start by going through what YAML looks like, at the very beginning of a company, or just in general of a software project, and how it can evolve over time into this day 101 scenario. Then what we'll do is we'll break down some abstract concepts of CI/CD. Maybe not some perfect abstractions, but just some ways to get us thinking about the CI/CD problem, and how to think about how to design these systems that may not involve YAML. Then we'll go into what this actually looks like in code. Then, lastly, we'll go over the takeaways of what we found.

Airbyte was Bart

Airbyte was Bart. We had 7000 lines of YAML across two repositories. We have a quasi monorepo setup at Airbyte. It was a rat's nest of untestable shell scripts. For those of you who are familiar with these systems, you may understand, yes, it's pretty easy to just inline a shell, whether it be Jenkins where you can just paste it into the window, or you can just throw it into your YAML, or you can call it from YAML as we'll see. There was a lot of subjective developer pain. We'd have a lot of people coming and complaining that our CI/CD was not great, it was not fun to use, it slowed everything down, and it was expensive. $5,000 a month may not be a lot for a lot of people in different organizations, but for us at the size of our organization, when we first approached this problem, which was about 40 to 50 engineers strong, it was a large bill. That gave us some motivation to really take a look and take stride at this problem. Then, 45-minute walk clock time, again, for some organizations, maybe this is not a lot. I think infamously it took 5 hours or something to build Chrome at one point. For us, being part of this fast feedback loop was important for us to be able to ship and iterate quickly. We looked at this as one thing that we wanted to look at improving.

YAML Day 1, YAML Day 101

Let's jump into day 1 versus day 101. This is how YAML can start off great and easy, and get out of control after a little while. You start off with a really simple workflow. You just got one thing, and it's one file, and it's doing one thing, and it works great. You built it, and you were able to get it up and running really quickly. It's simple and straightforward. It's YAML. It's easy to read. There's no indirection or anything. By day 101, because of the way that we've built out these actions, and these workflows. I'm using GitHub Actions terminology here, but it's applicable for other systems as well, where you can use YAML to call it, or YAML. Suddenly, you've got a stack. You've got a call stack in YAML, and you don't have jump to definition. It's difficult to work as a software engineer when you don't have jump to definition. You're afforded only the rudimentary tools of browser windows and jumping back and forth, and file names, and it's just a cumbersome experience. This is another example of GitHub Actions, again. It's got these units of code that are pretty great for certain things. This is one we use at Airbyte. It's on the left, you'll see test-reporter. What this does is it parses JUnit files and uploads them somewhere. It's a great piece of code. The problem with this is that there's a lot of complexity in this GitHub Action. There's probably maybe 4000 or 5000 lines of Python in just this GitHub Action, parsing the different coverage reports and stuff. When you have a problem that's within the action, you have to jump into the action itself. You've got to get this black box. Again, when there's only one, there's no problem. When it gets to day 101, and you have several hundred of these, suddenly, you've got a system where you don't really know all these third-party integrations that are around, and the tooling is not there to really tell you. There's just a bunch of black boxes of things that can just break for no reason. One very typical scenario we end up with at Airbyte is like some breaking change gets pushed to one of these actions, and it's not pinned properly, and you don't really have the tools or mechanisms to find it out except after the fact. Your CI/CD is just broken.

Another example. On the left here on day 1, we've got a very straightforward thing that is very realistic. This is what your first Node app build might look like. Again, it's easy to grok what's going on here. We're just installing some dependencies. We're running some tests. We're building a production version of this app. We're deploying it to stage. We're deploying it to prod. Very easy, very straightforward to understand. By day 101, when you're doing a bunch of these, in this example here, we've got 20-plus, it's really hard to understand. Again, you can't really isolate these things. You can't really test whether deploy to staging is working with these YAML scenarios. We don't even know if what we're going to change, what's going to run anymore. If you touch a single file in your repo, let's just say it's a single JavaScript file somewhere, how many of these on the right are even going to run, and where, and why? The tooling to surface this information for you is not there yet. One final example here. We have some simple Boolean logic examples on the left here. This is a very simple example where we just check and see if a JavaScript file was modified, and then we run the CI. On the right, we've got some actual Airbyte code that ran for a long time, to figure out whether or not we should run the deployment. You can see here, what we're trying to do is we're trying to express some complicated Boolean logic concepts in YAML. We end up being limited by the constructs of what this DSL is offering us. It can get very complicated and very hard to understand.

Pulling some of these ideas together, it's like, why is this becoming painful? In general, we have a lack of modularity. We don't have an easy way to isolate these components. We're limited to what YAML is doing, and we're limited to what the specific DSL is doing. They're not unit testable, so we don't really have a way of detecting regressions before we throw them up there. All we can do is run them against this remote system. We have some ecosystem related flaws. Everybody has their own quasi-DSL. If I take GitHub Actions code, and I want to run it in CircleCI, I can't just do that. I have to port it. Maybe some concepts don't exist in CircleCI, maybe some concepts don't exist in GitLab. It's not portable. It's intractable. It's this proprietary system where you don't really see all of the source code of what's going on, and there's these settings and configuration that are magical that come in. It's hard to emulate locally. The point I'll make here that I think it's key when we start thinking about CI/CD systems, is you want to be able to iterate quickly. This ripples out in so many different ways. I'm working in a platform engineering role right now. I want the developers I'm working for to adopt tooling, to be excited about working on tooling. When they can't work with the tools that they have locally, it's a very bad developer experience for them. There is a tool out for GitHub Action, specifically, that gets brought up about this, it's called act. act, it works. It's better than nothing. It has some limitations. Those limitations, they segue back into the fact that it's a proprietary system that it's trying to emulate, so, A, it's always going to be behind the pushes. B, there are just some things that just don't translate very well when you're running things locally.

CI/CD Breakdown

With that said, we've talked enough about pain points, let's break down what CI/CD is, and some maybe not perfect abstractions, but just some high-level system layers to think about. It's like, what is this YAML actually doing? We have this giant pile of YAML we've talked about, what is it actually doing? We've chosen to break this out into what I'm calling layers here. These are the six different roles that a system like GitHub Actions, or a system like GitLab, or a system like Circle is providing for you with all this YAML. It goes back to what Justin was saying about the complexity of CI/CD. There's a lot of stuff buried in here. When somebody talks about GitHub Actions, they could be talking about any one of these things. They could be talking about all of these things, or a subsection of these things. Pulling these out into some sort of layer helps us to make this into a more tractable problem, instead of just being able to say, it's complicated.

What are we calling an event trigger? This is how CI jobs are spawned. We're just responding to an event somewhere. Your CI job has to run somehow. This is pushing to main. This is commenting on a pull request. This is creating a pull request. This is the thing that tells your CI/CD system to do something. You can also think of this as the entry point to your application. Every CI/CD has this. One thing I want to call out, and we'll come back to later, is that this is going to be very CI/CD platform specific. What I mean by that is what GitHub calls an issue is not the same thing as what GitLab calls an issue, is not the same thing as what Jenkins calls their level of abstraction. There are all these terms, and there's no cohesive model where you can say, a GitHub Issue translates to a GitLab issue. Some of these concepts, they exist, and mostly match to each other, like a pull request and a merge request, but not all of them. Then, in the bottom here, I've added a rough estimate of how hard this was for us when we tackled these parts, these layers at Airbyte, so I added this 5% number here. This is an arbitrary number. I want to use it to highlight where we put the most effort and struggle into as we were figuring out the lay of the land on this. We put 5% here.

Authentication. You need to talk to remote systems in a CI/CD. You often need some secret injection. You're usually logging in somewhere. You might be doing a deployment. You might be uploading to some bucket. You might be doing some logging. Your system needs to know about this. This is important because if we're going to design something that's perhaps a little bit more locally faced, we need to have that in our mind when we think about secrets, how they're going to be handled on the local developer's machine, how we're going to do secret stores, and the like. The orchestrator, this is a big part. This is where we tell the executor what to run and where. Some examples are, I want to run some job on a machine. This is what jobs and steps are in GitHub Actions. This is also state management. You have the state passed. You have the state completed. You have the state failed. You have the state skipped. It's usually easy to think about this as a DAG. Some workflow orchestration is happening in your CI/CD, where you've broken out things into steps, into jobs. Then you're doing things based on the results. A very simple example here, again, GitHub Actions, is we've got build_job_1, we've got build_job_2, and then we have a test job that depends on build_job_1 and build_job_2 succeeding. We've got both parallelization and state management in this example.

Caching, this is typically tied to your execution layer. I'm saving nearly the best for last. It's hard. I think that most people that are engineers can resonate with that. It's very difficult to get right. It's important that you choose the right tooling and the right approach to do this. There are two broad categories that you deal with in CI/CD, one is called the build cache. Any good CI/CD system, you want your system to be able to act the same way, if you run it over again with the same inputs. Idempotent is the fancy term for this. If your CI system works like that, and you know what's going in, and you know the output already, because you built it an hour ago, or a week ago, then you don't even have to build it. That's the concept of the build cache. Then, the second part of this is the dependency cache. This is like your typical package manager, npm, PyPi, Maven, whatnot. This is just the concept of, we're going to be downloading from PyPi over again, why don't we move the dependencies either on the same machine so that we're not redownloading them or close by, aka in the same data center so that we can shuffle things into there quicker, and not rely on a third-party system. This is about 10% overall. When you think about it overall, for us, it was about 10% of the overall complexity.

The executor, this is the meat of your CI/CD. This is actually running your build, test, your deploy. This is what it looks like, a very simple example in GitHub Actions, or Circle. You're just executing a shell script here. That shell script may be doing any arbitrary amount of thing. This is a great example because it actually highlights how you can hide very important logical way in a shell script, and you don't really have a good way of jumping into it. A couple other examples is that you might run a containerized build with BuildKit. That's another way to run things. You can delegate to an entirely different task executor via command line call, like Gradle. For those of you who are in the JVM world, you're probably familiar with this tool. This is a very large part of your CI/CD system. We'll go into what we want to think about with this a little bit later. Lastly, is reusable modules. I put this in a layer by itself, because it's kind of the knot that ties the other ones together. It's the aspect of reusability that you need, because everybody is going to do common things. Why are we doing the same work as somebody else doing a common thing? For instance, an example I gave on here, is that everybody needs to upload to an S3 bucket probably. We have an action that you can just go grab off the marketplace, and it's great. You can just upload and you don't have to maintain your own code. Some other examples. This is the GitHub Actions Marketplace. Jenkins has this concept of plugins, which isn't exactly like the marketplace, but it's a similar concept. Then GitLab has some integrations as well that offer this. This is about 10% of the overall complexity of what to think about.

The Lisa Approach: CI/CD as Code

We managed to break it down. We picked some terms and we put things into some boxes so that we can think about CI/CD as something that we can put in some winners. Now we're going to talk about the Lisa approach. What is the Lisa approach? The Lisa approach is to think about CI/CD as something that we don't want to use YAML for. Something that solves these problems of scalability. Something that is actually fun to work on. Something that you don't have to push and pray. Anybody who's worked on GitHub Actions or Circle or Jenkins knows this feedback loop, where you're pushing to a pull request, commit by commit, over and over, just to get this one little CI/CD thing to pass. It's just like, now you've got 30, 40, 50 commits, and they're all little one-line changes in your YAML file. Nobody wants that. What if we had a world without that? How would we even go about doing? The first thing to think about here, tying it back to what we were talking about before, is that we want to think about local first. It comes back to the reasons that we mentioned before. It's that developers want it more. We get faster feedback loops, if we're not having to do the push and pray. As we mentioned earlier, it got pretty pricey for Airbyte to be doing CI/CD over again. If we can do those feedback loops locally on a developer's machine, it's much cheaper than renting machines from the cloud to do it. We get better debugging. We can actually have step through code, which to me is like a fundamental tool that every developer needs to have a proper development environment. If they cannot step through code, then in my opinion, you don't really have a development environment. Then, you get better modularity. You get to extend things and work on things, and going back to our key tool earlier, jump to definition.

One of the key design aspects that I wanted to call out that we learned when we were thinking about this at Airbyte, is that you need to start from the bottom layer, if you're going to design a CI/CD system. The reason for that is because, the caching, the execution layer, they're so critical to the core functionality of what your CI/CD is doing, that everything else at layer by layer on up needs to be informed by that. Just to give you some examples, I mentioned Gradle earlier. If you were going to build your reusable CI/CD system that was modular, on Gradle, you would have to make certain design decisions about the runner infrastructure that you are going to run on, the reusable modules. Are you going to use Gradle plugins? All of that stuff is informed by this bottom layer. That answer is going to be different than if you use, like what we did, is building it on BuildKit. There are different design decisions, different concerns, and they're all informed by whether or not you use Gradle, Bazel, or BuildKit, are the three that we're going to talk about.

The last key point that I want to make here is that, this concept of event triggers of like something is running your CI/CD, those, as I mentioned earlier, they're very platform specific. Again, Jenkins doesn't have the same way of running things and same terminology and same data model as GitLab does, and as GitHub Actions does, but the rest of the system doesn't have to be. We're going to introduce a way to box up our layers. This is going to be a simple way of thinking about it. The idea here is just, wrap them up in a CLI. Why? Because CLIs, developers like them. They're developer friendly. They know how to use them. They can be created relatively easy. There's a lot of good CLI libraries out there already. They're also good for a machine to understand. I'll demonstrate in a bit here that you can design a CI/CD system that basically just clears its throat and says, go CLI. That is the beauty of being able to wrap it up in something that a machine easily understands that's also configurable. This design approach tries to ride the balance between these. How can we make it machine understandable and configurable, but also still happy and available for developers? We'll start from the bottom up, and we'll talk about some of the design decisions we made in the Lisa story here at Airbyte. When we went through this design decision, we came from Gradle land. A lot of Airbyte is built on the Java platform. Gradle is a very powerful execution tool but it didn't work very well for us. We chose to use Dagger instead. What is Dagger? Dagger is a way for you to run execution in containers. It is an SDK on top of BuildKit. It's a way for you to define your execution layer entirely as code. One of the more interesting design decisions here is, because it's backed by BuildKit, it has an opt out caching approach. From a DX perspective, caching, going back to what we were saying before, it's a hard problem. There's a lot of different things that you have to do, to do caching right. We feel that giving developers caching by default is the right way. One of the interesting things to point out here is that when you think about your execution layer, and you think about your caching layer, they must fundamentally go together. That one of the limitations of GitHub Actions is that it's not. Caching is something you bring in entirely separate and it's something that's opt in. Choosing a tool, whether it be Dagger, or BuildKit, or Bazel, or Gradle, needs to have this caching aspect baked into the very core of what it is, for it to be successful. Again, caching, we leverage BuildKit under Dagger. There are a few other mature options. I mentioned them already, Gradle and Bazel. They each have their pros and cons. Gradle eventually ended up not working for us because it was too complicated for our developers to understand.

Let's talk a little bit about orchestration. Orchestration the Lisa way, going back to what orchestration is, this is an arbitrary term that we put on. It can be a general-purpose language construct in your design, or it can also be a specific toolchain. At Airbyte, we came from a very data transfer, data heavy specific background, so we chose Prefect for this. This could also just be your typical asynchronous programming language construct in your language of choice, whether it be Golang, whether it be Python, whether it be JavaScript. There are other tools out there. Gradle and Bazel have this aspect built into their platforms. There's also Airflow. It's a workflow orchestration tool. There are a couple others, Dagster and Temporal. Just a few examples of how you can get some visualization and things like that from your workflow orchestration tool are below. Lastly, the event triggers. These don't change. Because these are the very CI specific part, this is the part of the data model that is very Jenkins specific, very GitLab specific. We push anything that's specific to that model up into that layer, and it becomes a very thin layer. Then, running locally is just executing another event. By doing this, we can actually mock the remote CI locally, by mocking the GitHub Actions, in our case, events, by doing this. Lastly, remote modules. These are just code modules. This gives us all the things that we wanted before, in the Bart approach: jumping to definition, unit testable, no more black boxes. We can use our language constructs that are probably a bit better and a little bit more robust than GitHub Actions' distribution for our packaging. Instead, maybe I want to use Rust crates to do distribution. You can actually implement a shim for migration. One thing I'll bring up here is, maybe you want to move to a system like this incrementally. You can implement a shim that will invoke GitHub Actions' marketplace actions locally. The Dagger team is also working on a solution for this. I'm excited to see what they have to offer. Any code construct that you need to make for your end users, and one of the points that I want to drive home here is that, the important thing when you're thinking about CI/CD is that your end users need to want to be and very comfortable with using it. Being able to work with the language of their choice. The frontend people at Airbyte want to work in TypeScript, because that's what they're familiar with. Thinking about a solution that is comfortable for your end users is going to give you a lot more power, and you're going to see a lot more uptake when designing a CI/CD system for others.

Demo (aircmd)

Let's do a quick demo. What I'll do is I'll show a couple quick runs of the CLI system. We will also do a quick step through of the code just to see what this actually looks like when you start to abstract some of these ideas out. This isn't maybe the only canonical way to approach this, but it is a way. We've got a CLI tool that I built over at Airbyte, it's called aircmd. This is just a click Python wrapper. It just has some very simple commands. It builds itself and it runs its own CI. What we're going to do is we're just going to run a quick command to run CI. The interesting thing here is, when we do this, one thing we can do from a UX perspective is to make these commands composable. What I mean by composable, and we'll look at this in the code, is that there's actually three things happening here: build, test, and CI. The CI does some things but it calls test. The test does some things but it calls build. Your end users can get little stages of progress as they run. If they only care about the build stage, they only have to run the build stage. They don't have to wait for anything else. You can see here that a lot of stuff was cached. What you're seeing here is actually BuildKit commands. We can actually go look at the run. This is the Dagger cloud here. We can see it here. You can see, this is one of the DX things that I think is incredibly important to highlight when it comes to caching, because caching is so difficult. Being able to see what's cached and what's not easily is pretty key to having your end users be able to write performant code. Here, you can see that almost all of this run was cached, so it took almost no time at all. Then, what we can do is we can actually bust the cache. We're going to go take a quick peek at the code over here. We're going to take a quick look at the code. Let's go ahead and start at ci.yaml. This is the event triggers layer that we were talking about earlier. There's some minimal stuff in here. The idea here is that you have some CI/CD specific layer, but all it's doing is essentially calling CI. You can see here that that's what we're doing. We're setting some environment variables. We're clearing our throat, and we're running the CLI. Then once we do that, we get into the orchestration layer. I left auth out of this demo for purposes of brevity, but you can think of auth as just being a module in this setup. In our orchestration layer, we've defined three commands. Those three commands, they're being submitted asynchronously, and they're calling each other. This is just regular Python. We're leveraging some Prefect constructs, but under the hood, it's just regular Python. Then we jump into the execution/caching layer. Then, what's happening here is interesting. Essentially, what we're doing is we're loading, you guys know of them as Docker containers, but OCI compatible client containers. We're loading those, and we're just running commands inside of a container. Now we get unit testability. We get the ability to specify cache layers. One easy way to think about what's going on here is each one of these commands is not perfect, but it's roughly analogous to a Dockerfile run command when it's happening under the hood. You could replicate this with buildx in Docker as well. It's not quite one to one but pretty close. What are we going to do? We're going to do something that busts the cache. What's happening is we're taking files, we're loading them into a container. If I just make a change, we'll just add a whitespace, maybe a comment, it's going to change the inputs to the container. Remember, we go back into what was happening before, we talked about caching. If the inputs change, we're not cached anymore. We're going to rerun the same command after we've added our comment, and we're going to see that it's not cached. It's going to actually do some work here, instead of skipping everything.

Now we'll take a quick look at the visualization tool here. These are hosted in the cloud. They can be local, but they're hosted in the cloud. What's happening is the client is actually streaming this data to them in real time, so we can actually see the job running in real time, as we go along. You can see we've already gotten pretty far in this one, and it's running the pip install. That's the core facet of the demo here is that we want to demonstrate that we have some tools to visualize what's going on. We want to give our end users the ability to peek into the core facet of our CI/CD system, which is the cache layer here. Again, once we're in, we can just jump through all of our code. If we want to see what these commands are doing, we just go to definition, we get our jump to. We have a stronger DevEx than we did with just pure YAML. We can walk all the way down the stack to the BuildKit base.

Key Takeaways

Let's talk about Lisa's light at the end of the tunnel. What does this actually mean for Airbyte? We spent some time and we refactored some stuff, what results were we able to show? How were we able to improve things for our end users? This is the output of the tool clock, see count lines of code. Over the course of three months, we were able to reduce the amount of YAML by 50%. What we did is we worked towards this incrementally. We're still incrementally working towards it. We picked the nastiest, most painful builds first. We wrote up a proof of concept that was a very thin framework that handled those nasty, painful builds. Then we incrementally started bringing things over. We added unit tests every step of the way. One of the key performance indicators that we wanted to make sure was count the regressions. How hard is it to make a pipeline that doesn't regress? We were able to achieve 90% cost savings. Part of this isn't really indicative of the tooling that we chose, but more that we were able to leverage the tooling that we chose and the system that we made to be able to really easily go in. Going back to looking at the caching GUI from earlier, we were able to really easily point in and go identify bottlenecks for where caching was being really painful. To describe exactly what this meant, is, for instance, we were trying to test 70 connectors, which is just a container output that gets tested every single day. In the previous time, in the Bart times, we were spawning one machine in parallel in GitHub Actions for each one of those connectors, which was a very expensive endeavor. After we did this, we were able to leverage caching such that the layers were all shared within each other, and we switched to a single machine approach. In the time it took all 70 connectors to run wall clock, we were able to get that same time on one machine. That was a 70-time reduction in machine time. That's how we were able to achieve such massive cost savings. We're able to test connectors more often. Our developers are much happier because they have an environment where they can actually debug problems. We've gotten away from push and pray, which for us was a major DX win.

Pulling it all together, YAML is a great tool. It works really well when you're doing something simple. In some cases, it's going to be enough. If all you're doing is maybe you've got like a side project, or what you're doing is never going to be very complex, maybe YAML is the answer. When you start to scale, when you start to grow beyond a certain point, it really starts to fall apart. For us, it happened right at about the time that our system became complex enough. Day 101 was probably at year 1.5 for us. It did take a while for us to really sit back and acknowledge it. It's key to recognize when you're starting to get into that area of when it's going to be a little crazy. We saw some major reduction of machine days, and just had some huge cost savings wins, huge performance wins. When you think about this, again, think about it from the ground up. Think about it from the execution and caching layer first. Because how you actually run these things on a remote system has a lot of nuances and caveats as well. It's like, ok, I have a cache, how do I get it onto the machine where it's running in a fast and easy way? Build the tooling and constructs your developers know. This goes back to the Airbyte frontend people want to run in TypeScript. Giving people an environment they're comfortable with is going to give you adoption and mindshare and power for your developers to feel empowered and feel like they want to work on the system instead of it being something that just gets pushed to the wayside in a lot of organizations. Incremental migration is possible. When you take this whole thing, this whole blob, it seems a little intimidating. There's a lot of moving gears in a CI/CD system. It is possible to build a bare bones thing. What we did is we built a bare bones system, and then we ran in parallel the same job for a week or two, and then we switched. The last thing is, get away from push and pray. We want to be able to do this locally.

Questions and Answers

Participant 1: I feel like you alluded this earlier in your talk, but like this tends to be a slippery slope. It's like, you start off with one YAML file, and then there's like some more that occur down to the 101st day, where things are like launching far. At that point, it just always feels like such a lift. It's like, go. It's like, ok, that's it, stop this madness [inaudible 00:39:27]. As if you are just piling on. Organizationally, personally, how do you solve that problem? How do you get buy-in for like, saying, this is madness, this needs to stop. How do you do that?

Barber: This was definitely something that came up at Airbyte as well. The key methodology for success in our case was to demonstrate the win. Especially in this area, there's a lot of like, this is the way that people do CI/CD. When you're coming to the table with a new approach, you need to be able to demonstrate a very dramatic win, like we were able to do. Finding the right proof of concept is pretty key. For us, it was finding the nastiest, slowest, flakiest CI/CD job and saying, let's just take a week, a week and a half and spike out what this can look like. Just getting something out there that's bare bones that shows some very dramatic wins, I think is the key political way to help drive adoption and get people to see the possibility of what you can do.

Participant 2: Was there anyone who actually pushed back and said, "I like YAML."

Barber: I think that some people do appreciate the simplicity of it. There are some benefits to being able to have something that's standardized and quick and easy to grok. Contextually, once you get to a point in the organization where you're doing, in some of our most egregious examples, they were inlining Python code in the YAML. It was very easy to see for people in our organization, how it had gotten out of control. Maybe other organizations haven't gotten to that point yet. When it is bad, it's very obvious that it's bad.

Participant 3: Has the developer experience of not having to repush and iterate on CI, has that changed the developer's thinking about how CI should be?

Barber: Yes. In our organization, there's been a larger adoption, just a big push in general towards local first tooling. This is part of that story. There are some organizations I've worked in, in the past, where it gets shoved over to the DevOps team, the build does. What we're seeing in our organization is that more people are becoming more actively involved in wanting to work on these systems and contribute to them. Really, our pie in the sky goal from the get-go was to bridge these gaps between different organizations and not just have the DevOps guys who do the pipeline. That everybody works on the pipeline together. We're all engineers and we all work on the platform together.

Participant 4: One thing I would like to add is the portability of your solution that you're using. Because at GitLab and Azure DevOps and company 1000 people on one, 1000 people on the other, and try that into migratable platform because they have two teams maintaining the stuff and running the stuff, and putting this into a good CI/CD tool independent approach depends on [inaudible 00:43:58].

Barber: That was something that when we looked at different ways to approach this, we looked at some other platforms and stuff like Earthly and the like. Being able to take a step back and not be married to one specific platform was a big selling factor for us.

Participant 4: The cost of moving from one YAML definition to the other, it's just lost work for the teams, and it's very painful. This just might be a way out of this, and going to the marketplace with this sort of tool is still very volatile. Where Jenkins, or some predecessor [inaudible 00:44:44] different tools, will be imperative to have something in there, vendor independent in place.

Participant 5: What do you see as next steps? You've got Air, what are the pains you still have? What's the roadmap forward?

Barber: Where's the long tail in this? No solution is perfect. Where we have hit the long tail, that we're hoping that there can be some improvement, is especially if you work in a microservices-like setup. I think this is just an unsolved problem in DevOps, CI/CD systems in general is, if you want to have these reusable modules, whether they be your own, because you'll need to make your own or whether they're maintained by somebody else, how do you manage the distribution of all these modules? Let's take a typical microservices example, like Bart's example. He's got 30 different services. How do we manage this tool, and the versions of this tool, and all the packages and all the plugins and all of that, and be able to deploy updates and test those updates, and do that in a robust manner. That's the longer tail challenge of what we're still coming up and against. Trying to tackle that has been one of our priorities, because a lot of work is on the platform engineers to maintain that ecosystem. Being able to solve that problem would take a pretty big load off of the platform engineering orgs.

Participant 2: Is there any disadvantages to using Dagger? I've definitely seen that, you see like YAML and Kubernetes, and something like Pulumi. There's always advantages for code versus declarative information. I'm super [inaudible 00:46:48], but I haven't played with it much yet. Is there any footguns I should be aware of, or any challenges?

Barber: Part of it is that it's a very new technology. It has all the caveats that come with new technology. I think the one thing when it comes to Dagger, and in general just BuildKit that we've run into challenge wise is, it's a new concept for a lot of people. BuildKit itself is relatively new technology as well. Getting people to think about things the container way may be a challenge for some organizations that are unfamiliar with it. For us, coming from Gradle, it felt like less of a lift, because Gradle, for us, it was almost interminable sometimes. The two primary challenges that we had with Dagger were that it's new technology, it has all the facets of that that come with it. Containerized builds like these are just new technology in general and people aren't as familiar with them.

This talk was recorded at QCon San Francisco 2023

Recorded at:

Jul 31, 2024

Conor Barber

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2024 CPSS SoDaRP Conference and Banquet

This year's banquet will be held on Monday, September 30, 2024 , at McCrory Gardens in Brookings.

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Conference Program

Presentation 1: 8:15-9:15 a.m..

• Introduced by: Austin Hoekman

Speaker: Molly Brown, EVP of Corporate Strategy, GenPro Energy Solution, Piedmont, SD

• ABSTRACT : The Scottsbluff Solar Project was developed and constructed by GenPro and Sol Systems with output contracted to Nebraska Public Power District. The project was installed in 2020 and is a 4.375 MW AC community solar project available to NPPD customers in the area with over 14,000 solar panels on trackers. In June 2023, the project was decimated by a severe hailstorm that brought near softball-sized hail to the facility. In January 2024, the damage sustained by the project had been repaired/replaced and was returned to service. This presentation will discuss the project and design details, damage sustained, and steps to rebuild with a focus on design considerations for solar development in our regions related to reliability and resiliency.

Presentation 2: 9:30-10:30 a.m.

• ABSTRACT : – For most Transmission Owners, it is less than one year from the effective date of FERC Order No. 881 which will dramatically change Facility Ratings “business as usual.” Whether ambient-adjusted ratings (AAR), accounting for ambient temperatures and day/night solar heating, or dynamic line ratings (DLR), using a myriad of current ambient conditions, Transmission Owners face significant challenges updating policies, records, and data systems to ensure proper development and application of their Facility Ratings programs towards meeting impending obligations. Representing a Transmission Operator as well as a large Transmission Owner with over 7,800 circuit miles of Bulk Electric System, Chris is responsible for efforts to comply with FERC directives and will present recent efforts to be ready for AAR and DLR.

Presentation 3:10:45-11:45 a.m.

• Introduced by: Chris Graff

• ABSTRACT : Inverter-based DERs are seen by some as a black box of power electronics and a mystery to how they respond to the electric power system at high penetration levels and during abnormal system conditions. Unfamiliarity causes uncertainty driving conservative decisions that may have been different if more was understood. The purpose of this presentation is to cover the rapid expansion of inverter-based DER interconnections and their impact on the electric power system. Topics will include the current state of DER, System Impact Study and Hosting Capacity, PV & BESS Installations, Inverter Controls, Capabilities and settings.

Lunch Break: 11:45 a.m.-1 p.m.

Presentation 4: 1-2 p.m.

• ABSTRACT : Many grants have become available in the past several years for projects ranging from utility infrastructure to renewables to electrification and energy efficiency. These provide opportunities for utilities. They also provide opportunities for your customers, thereby impacting your utility. This presentation will talk about current opportunities with a focus on both preparation for applications your utility could submit as well as awareness of activities your customers may be pursuing.

Presentation 5: 2:15-3:15 p.m.

• Introduced by: Andrew Hora

• ABSTRACT : Oahe Electric Cooperative supplies electricity to Big Watt Digital, the largest cryptocurrency and data center in South Dakota, with a peak capacity of 30MWs. We utilize a diversified generation portfolio that prioritizes avoiding future generation capacity costs and enhancing grid resiliency through peak load curtailment capabilities.

Presentation 6: 3:30-4:30 p.m.

• ABSTRACT : Xcel has long been a national leader in wind energy. By early 2021, we became one of the first energy providers in the United States to reach 10,000 MWs of wind power on our system. In 2018, we were the first electric company in the nation to commit to 100% carbon-free electricity. That commitment had two parts – first to achieve 80% carbon reduction (measured from 2005 levels) by 2030 and then to achieve a fully carbon-free electricity system by 2050. This is requiring changes not just to our generation fleet but to the transmission and distribution grid as well. This presentation will cover some of our concerns and some of our plans to address them.

Banquet Program

5:30 p.m. - Social 6 p.m. - Banquet

Welcome and Invocation                     Dr. Steven Hietpas                     Professor of Electrical Engineering | CPSS Coordinator 6:45 p.m. - Presentation of the CPSS Scholarship Awards                 - Presentation of Wayne E. Knabach Excellence in Power Award      7 p.m. - Keynote Presentation

CPSS Scholarship Awardees

Kalen George Meyer is a senior electrical engineering student at South Dakota State University, set to earn his Bachelor of Science degree in Electrical Engineering in May 2025. During the summer of 2024, Kalen interned for the fourth time at DGR Engineering in their electrical department. His responsibilities included creating protection and control drawing packages and overseeing substation station service and transformer fan modifications. At SDSU, Kalen has been actively involved in Eta Kappa Nu, serving as president during his senior year. He is also a member of Tau Beta Pi, IEEE, and the SDSU Wrestling team. After graduation, Kalen plans to pursue a career in the power industry.

Luke Alan Rasmussen is a senior electrical engineering student at South Dakota State University anticipating earning his Bachelor of Science Degree in May of 2025. Luke spent the summer of 2024 interning at DGR Engineering in their electrical department, primarily working on protection and controls design for substations. This was his third summer interning at DGR. While at SDSU, Luke has been part of the South Dakota Beta chapter of Tau Beta Pi, serving as the Joint Engineering Council representative. Other organizations include Eta Kappa Nu, IEEE and Robotics Club. After completing his degree, Luke plans to pursue an engineering career in the power industry.

Excellence in Power Awardee

Michael W. Sydow NorthWestern Energy, Huron, SD General Manager of Operations for South Dakota and Nebraska (retired)

Mike Sydow has 38 years of experience with NorthWestern Energy (NWE), serving as an Operations leader with the utility and as an officer in Energy Solutions. He holds a BSEE from SDSU. During his assignments at NorthWestern Energy, he was responsible for the design and implementation of substation additions as well as voltage conversion projects in the Aberdeen Division. For many years, Mike was responsible for successful labor relations, contract negotiations, and grievance resolutions with the IBEW for SD/NE at NorthWestern Energy. Turning SD/NE’s lost-time incident rate from a high of twenty-three incidents in 2003 to zero in 2012-2016, is an accomplishment Mike and SD/NE employees have been very proud of. Mike assumed lead roles in large-scale Operations events including, the 2005 ice storm, 115kv transmission line collapse west of Groton, tornado Tuesday wind damage, Midwest Mutual Assistance Group nation-wide resource assistance and coordination, and resource/material oversight for several tornado/straight-line wind events across NorthWestern Energy’s territory. During Mike’s tenure, South Dakota NorthWestern Energy attained and retained a reliability rating that was in the first quartile of the first quartile, in overall CAIDI, SAIDI and SAIFI indices. Mike enjoyed working with Engineering and Legal co-workers to create opportunities for NorthWestern Energy to electrically serve large-scale industry prospects such as Dakota Access Pipeline and SD Soybean Processors Inc. He enjoyed working with Lake Area Technical Institute and Mitchell Technical Institute as an industry program advisor in the Powerline and Control Systems programs, where Mike encouraged and participated in the development of new options, including substation technician and electric control system operator.

Mike was the Southern Region Manager in Energy Solutions (a NorthWestern subsidiary that assisted large-scale companies with energy utilization technologies), and went on to become the VP of Engineering and Delivery. While in this capacity, he worked with large-scale electric customers to develop power factor correction solutions, HVAC solutions, and sought out emerging technology applications.

Mike and his wife Tammy live at Pickerel Lake in northeastern South Dakota. His son John (Brooke) Sydow is a SDSU Project Management graduate and is employed at East River Electric Coop as their Project Services Manager. Mike’s son Kyle (Jenny) Sydow is a SDSU Mass Marketing and Communications graduate and is employed by Daktronics as the High School/Parks and Recreation Market Manager. Mike’s daughter Allie is an SDSU Graphics Design graduate working as a professional baker in Sioux Falls.

Keynote Presentation

Richard McComish -- President, ECI Billings, MT

Engineering and Construction Support of HV Power System Reliability

Richard L. (Dick) McComish started his engineering career in January 1976 with SSR, a small, regional consultant located in Montana. He gained exposure in the electrical power industry as a design and field engineer, project engineer, commissioning engineer, project manager and engineering manager on a range of engineering needs for rural electric cooperatives. In 1990, Dick had the opportunity to acquire a majority ownership in a four-person consulting firm, Electrical Consultants, Inc. (ECI), becoming president of the firm in 1993. His love for engineering and business led to forming EPC Services, a professionally led design-construct firm serving the needs of utilities and the energy industry across the nation. He has overseen the growth of ECI and EPC Services to rank among the top ten T&D power delivery and EPC firms. Today, the firms have over 1,000 employees and 30 regional offices nationwide with revenues exceeding $700 million annually.

Mr. McComish is a licensed professional engineer in over thirty states. He holds a B.S. in Electrical Engineering, Power Emphasis, from South Dakota State University, where he graduated in December of 1975. Throughout his decades-long career, he has been recognized by his peers with the Young Engineer of the Year Award and later, the Distinguished Service Award by the Montana Society of Engineers. He was also recognized by his alma mater as the 2015 recipient of the Wayne E. Knabach Award for Excellence in Power.

Dick and his wife Karen live in Billings, Montana. They have three children and five grandchildren. In addition to his passion for engineering, Dick enjoys professional tennis, classic cars, good rum and fishing with friends, along with the musical musings of Jimmy Buffet.

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Autodesk Vault: Get your data under control with PDM

Streamline workflows with product data management software.

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• Software bundle
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• Product versions

What is Autodesk Vault?

Autodesk Vault product data management (PDM) software integrates with Autodesk design tools and other CAD systems to keep everyone working from a central source of organized data. Use Autodesk Vault to increase collaboration and streamline workflows across engineering, manufacturing, and extended teams.

Automate design and engineering processes.

Control what people can access and edit.

Track revisions and design history.

See system requirements

Vault PDM overview (video: 1:58 min.)

Why use Autodesk Vault?

Stop searching and start designing.

Quickly find and reuse design data and minimize rework and repetitive tasks.

Unify teams and boost productivity

Accelerate workstreams in a system that brings together internal and external collaborators.

Increase product development agility 

Achieve faster response times and fewer errors with automation and data accessibility.

What you can do with Autodesk Vault

Standardize data and processes

Tools for administrators, including configurable email notifications, help drive greater organizational standards for data creation, review and release processes, and industry standards.

Vault Gateway overview (video: 1:38 min.)

Work with your data anywhere on any device

Stay connected and productive wherever you need to work. Access Vault data securely without the need for a VPN connection using the Vault client and mobile app with Vault Gateway.

Project Sync overview (video: 2:13 min.)

Enhance cloud and remote collaboration

Enable collaboration capabilities across stakeholders using Project Sync with Autodesk Inventor and Fusion. Share native files and design updates bi-directionally while maintaining access permission control, versioning, and traceability.

Product lifecycle management with Vault PLM

Extend data and processes when you bundle Vault Professional with Autodesk Fusion Manage, a powerful cloud-based PLM solution for managing new product introductions, quality, supplier collaboration, requirements, and more. Talk to your Autodesk Sales rep or reseller about Vault PLM.

For more information:

“What used to take our engineers 3-5 days now takes as little as 20 minutes.”

—Ben Holmes, Digital Design Manager, NOV FGS

“Being able to manage our items and files related to each item is extremely valuable.”

—Kipp Sakundiak, General Manager, Prairie Machine, Rokion parent company

“You’re not able to make mistakes or forget small valves or O-rings if all parts are stored within Vault.”

—Lune Riezebos, Application Specialist in Service Delivery, GEA

“I knew we had to automate more of our processes, because we had so many profiles and so much documentation ...”

—Carl Schelfhout, PDM/PLM and Process Manager, Reynaers Aluminium

Which Vault is right for you?

Vault Professional

Advanced enterprise product data management software that connects distributed teams with multisite, multi-CAD collaboration and delivers valuable insights.

Vault Office

Document management for non-CAD users. Vault Office integrates with Microsoft Office Word, Excel, PowerPoint, and Outlook.

Vault Basic

Design file management to help you automate data creation and organize documentation. Available only with a subscription to the Autodesk Product Design & Manufacturing Collection. 

Connect your organization’s people, processes, and data. Vault PLM combines Vault Professional with Autodesk Fusion Manage for enterprise-wide collaboration and product lifecycle management.

Explore Vault resources

All things pdm and plm.

Read about Autodesk data and process management tips and tools.

YOUTUBE CHANNEL

TheVaultKnowsAll 

Watch videos on the latest release, including productivity tips, on our YouTube channel.

PRODUCT ROADMAP

What’s next for Vault

See upcoming new features and enhancements from the Vault product team.

Webinar collection: Data and process management

Discover the features and benefits of Autodesk PDM and PLM solutions, and get your questions answered by the experts in this webinar collection.

Frequently asked questions (FAQs)

What is autodesk vault used for.

Vault PDM integrates with Autodesk design tools and other CAD systems and is used for managing data and automating design and engineering processes. 

Vault helps ensure that everyone is working with the most up-to-date information in a system that automatically tracks changes, maintains past file versions, and captures the entire history of your designs. 

Multisite functionality, available with Vault Professional, enables companies to synchronize design data among distributed workgroups across locations, geographies, and the entire organization.

Who uses Autodesk Vault?

Autodesk Vault is used by engineers, designers, and extended teams to streamline workflows and speed up product development. Everyone works from a central source of organized data—collaborating, reducing errors, and saving time.

What is Vault PLM used for?

Autodesk Vault PLM combines Vault Professional with Fusion 360 Manage to provide enterprise-wide collaboration for all involved in the product lifecycle—from engineering and supply chain to quality and manufacturing. Organizations use Vault PLM to digitally transform product development workflows to achieve better business outcomes, such as reducing time wasted on non-value add tasks, improving product development agility, and bringing better products to market faster. 

What is the difference between Vault Professional and Vault Office?

Vault Professional is for CAD users to manage design and engineering data and processes whereas Vault Office is for non-CAD users to manage documents.

Which operating system does Vault run on?

Vault runs on Microsoft® Windows®. See Vault system requirements for details.

Which versions of Vault can I use if I subscribe to the current version? 

Your Autodesk Vault subscription gives you access to install and use the three previous versions. Available downloads are listed in your Autodesk Account after subscribing. See also  previous releases available for subscribers .

Can I install Vault on multiple computers? 

With a subscription to Autodesk Vault software, you can install it on up to three computers or other devices. However, only the named user can sign in and use that software on a single computer at any given time. Please refer to the Software License Agreement for more information.

How much does an Autodesk Vault subscription cost?

. If you have infrequent users and are interested in a pay-as-you-go option, please visit www.autodesk.com/flex to learn more.

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